JP6308922B2 - Rolling bearing abnormality diagnosis apparatus, wind power generation apparatus, and rolling bearing abnormality diagnosis method - Google Patents

Description

  The present invention relates to an abnormality diagnosis device for a rolling bearing, a wind power generation device, and an abnormality diagnosis method for a rolling bearing, and more particularly to an abnormality diagnosis technology for a rolling bearing provided in a main shaft, a speed increaser, a generator, and the like of the wind power generation device.

  In a wind power generator, power is generated by rotating a main shaft connected to a blade that receives wind power, rotating the main shaft with a speed increaser, and then rotating a rotor of the power generator. Each of the main shaft, the speed increaser, and the rotating shaft of the generator is rotatably supported by a rolling bearing, and an abnormality diagnosis device for diagnosing such an abnormality of the bearing is known.

  As an abnormality diagnosing device, a device that performs an abnormality diagnosis by detecting vibration of a bearing by an acceleration sensor provided in the bearing is well known (for example, see Patent Documents 1 to 3). For example, an abnormality diagnosis device disclosed in Japanese Patent Application Laid-Open No. 2006-105956 (Patent Document 1) is an abnormality diagnosis device that diagnoses an abnormality in a double row tapered roller bearing incorporated in a rolling bearing device for a railway vehicle. A drive motor that rotationally drives the double row tapered roller bearing, and an acceleration sensor attached to the bearing box. An abnormality of the double-row tapered roller bearing is diagnosed based on a detection signal from the acceleration sensor during inertial rotation within a predetermined rotational speed region of the double-row tapered roller bearing when the drive motor is not energized (Patent Document 1). reference).

JP 2006-105956 A JP 2009-20090 A JP 2011-154020 A

  When various moving parts such as a gear box and a motor are connected to the rotating body supported by the bearing, an acceleration sensor, a vibration sound sensor, or the like causes S due to vibration from the moving part or vibration due to contact with the moving part. The / N ratio may be small, and it may be difficult to detect a bearing abnormality, or the abnormality detection logic may be complicated.

  On the other hand, abnormality diagnosis based on the displacement between the inner and outer rings of the bearing is known. When the displacement between the inner and outer rings increases, the relative positional relationship between the rotating body supported by the bearing and its adjacent parts changes, which may affect the adjacent parts. For example, when the main shaft supported by the bearing is connected to the gear box, if the displacement between the inner and outer rings of the bearing increases, a predetermined backlash provided between the gear integrated with the main shaft and the gear on the other side is generated. Change, and poor meshing can occur. Therefore, the increase in displacement between the inner and outer rings is considered to be strongly related to the replacement time of the bearing, and abnormality diagnosis based on the displacement between the inner and outer rings is suitable as a bearing abnormality diagnosis method.

  When the bearing abnormality diagnosis is performed based on the displacement between the inner and outer rings, the abnormality diagnosis is possible by comparing with the displacement amount when the bearing is normal, such as immediately after installation of the equipment. This is because if the bearing is damaged, the internal clearance increases, and the amount of displacement between the inner and outer rings varies greatly between normal and abnormal conditions. Here, the measured value of the displacement sensor that detects the displacement may change before and after the maintenance and repair of the equipment. In other words, the displacement sensor measurement value (absolute value) changes before and after maintenance or repair by adjusting the installation state of the displacement sensor and measurement object or performing calibration of the displacement sensor during maintenance or repair. obtain. Such a change in the measured value of the displacement sensor before and after maintenance may reduce the accuracy of abnormality diagnosis based on the displacement sensor.

  The present invention has been made in order to solve such a problem, and the purpose of the invention is rolling that can realize a highly accurate abnormality diagnosis even if the installation state of the displacement sensor and the measurement object changes due to maintenance or repair. An object of the present invention is to provide a bearing abnormality diagnosis device, a wind turbine generator, and a rolling bearing abnormality diagnosis method.

  According to this invention, the abnormality diagnosis device for a rolling bearing includes a displacement sensor and a diagnosis unit. The rolling bearing is constituted by a ball bearing or a roller bearing having a contact angle. The displacement sensor detects a relative displacement between the inner and outer rings of the rolling bearing. The diagnosis unit performs abnormality diagnosis of the rolling bearing based on the detection value of the displacement sensor. Then, the diagnosis unit calculates a value detected by the displacement sensor under a first axial load condition for the rolling bearing and a value detected by the displacement sensor under a second axial load condition different from the first axial load condition. An abnormality diagnosis is performed based on the difference.

  In this abnormality diagnosis device, abnormality diagnosis is executed based on the difference between the detection values of the displacement sensor under two different axial load conditions. By using the difference in displacement under different load conditions, even during equipment maintenance after collection of normal data, which is comparative data for performing abnormality diagnosis, during measurement under different load conditions If not, the change in the detection value of the displacement sensor accompanying maintenance or the like has little influence on the abnormality diagnosis result. Further, in this abnormality diagnosis apparatus, two different load conditions can be easily set by using the axial load condition. For example, in the main bearing of a wind turbine generator, the rotation of the blade and the axial load of the main bearing are correlated, and two different axial load conditions can be easily set based on the rotation state of the blade. Therefore, according to this abnormality diagnosis apparatus, even when the installation state of the displacement sensor and the measurement object changes due to maintenance or repair, a highly accurate abnormality diagnosis can be realized.

  Preferably, the rolling bearing is used as a main bearing of the wind power generator. The first and second axial load conditions are determined based on the power generation amount of the wind turbine generator.

  Preferably, the first and second axial load conditions are determined based on the rotational speed of the rolling bearing.

  Preferably, the rolling bearing is used as a main bearing of the wind power generator. The first axial load condition is satisfied when the rotation of the blades of the wind turbine generator is stopped. The second axial load condition is satisfied when the wind turbine generator is performing rated operation.

  Preferably, the displacement sensor detects a relative displacement between the inner and outer rings in the rotation axis direction of the rolling bearing.

  Further, according to the present invention, the wind power generator includes a blade that receives wind power, a main shaft connected to the blade, a generator, a plurality of rolling bearings, and an abnormality diagnosis device. The generator is connected to a main shaft or a speed increaser for increasing the rotation speed of the main shaft. The plurality of rolling bearings are provided on the main shaft, the speed increaser, and the generator. The abnormality diagnosis device diagnoses at least one abnormality of the plurality of rolling bearings. A target bearing that is a target of abnormality diagnosis by the abnormality diagnosis apparatus is configured by a ball bearing or a roller bearing having a contact angle. The abnormality diagnosis device includes a displacement sensor and a diagnosis unit. The displacement sensor detects a relative displacement between the inner and outer rings of the target bearing. The diagnosis unit performs abnormality diagnosis of the target bearing based on the detection value of the displacement sensor. Then, the diagnosis unit calculates a detection value of the displacement sensor under the first axial load condition for the target bearing and a detection value of the displacement sensor under the second axial load condition different from the first axial load condition. An abnormality diagnosis is performed based on the difference.

  According to the present invention, the abnormality diagnosis method is an abnormality diagnosis method for a rolling bearing constituted by a ball bearing or a roller bearing having a contact angle, and is a rolling bearing under a first axial load condition for the rolling bearing. Detecting a first displacement amount indicating a relative displacement between the inner and outer rings, and detecting a second displacement amount indicating a relative displacement under a second axial load condition different from the first axial load condition. And diagnosing an abnormality of the rolling bearing based on a difference between the first displacement amount and the second displacement amount.

  According to this invention, even if the installation state of the displacement sensor and the measurement object changes due to maintenance or repair, it is possible to realize a highly accurate abnormality diagnosis.

It is the figure which showed schematically the structure of the wind power generator to which the abnormality diagnosis apparatus of the rolling bearing by embodiment of this invention is applied. It is a figure for demonstrating the example of installation of a displacement sensor. It is a figure which shows the relationship between the axial load in a bearing, and the origin moving amount | distance in the rotating shaft direction. It is a flowchart explaining the procedure of the normal time data collection process performed by a data processor. It is a flowchart explaining the procedure of the abnormality diagnosis process performed by a data processor. It is a figure which shows the relationship between the axial load in a bearing, and the origin moving amount | distance in a bearing radial direction. It is a flowchart explaining the procedure of the abnormality diagnosis process in the case of determining whether an axial load condition is materialized based on the electric power generation amount of a generator. It is a flowchart explaining the procedure of the abnormality diagnosis process in the case of determining whether the axial load condition is established based on the rotation speed of the main shaft.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the following description, the same or corresponding elements are denoted by the same reference numerals, and detailed description thereof will not be repeated.

  FIG. 1 is a diagram schematically showing the configuration of a wind turbine generator to which a rolling bearing abnormality diagnosis device according to an embodiment of the present invention is applied. Referring to FIG. 1, a wind turbine generator 10 includes a main shaft 20, a blade 30, a speed increaser 40, a generator 50, a main shaft bearing (hereinafter simply referred to as “bearing”) 60, and a displacement. A sensor 70 and a data processing device 80 are provided. The step-up gear 40, the generator 50, the bearing 60, the displacement sensor 70, and the data processing device 80 are stored in the nacelle 90, and the nacelle 90 is supported by the tower 100.

  The main shaft 20 enters the nacelle 90 and is connected to the input shaft of the speed increaser 40 and is rotatably supported by the bearing 60. The main shaft 20 transmits the rotational torque generated by the blade 30 receiving the wind force to the input shaft of the speed increaser 40. The blade 30 is provided at the tip of the main shaft 20 and converts wind force into rotational torque and transmits it to the main shaft 20.

  The speed increaser 40 is provided between the main shaft 20 and the generator 50, and increases the rotational speed of the main shaft 20 to output to the generator 50. As an example, the speed increaser 40 is configured by a gear speed increasing mechanism including a planetary gear, an intermediate shaft, a high speed shaft, and the like. Although not specifically illustrated, a plurality of bearings that rotatably support a plurality of shafts are also provided in the speed increaser 40. The generator 50 is connected to the output shaft of the speed increaser 40, and generates power by the rotational torque received from the speed increaser 40. Although the generator 50 is comprised, for example with an induction generator, the kind of generator 50 is not limited to this. A bearing that rotatably supports the rotor is also provided in the generator 50.

  The bearing 60 is fixed in the nacelle 90 and rotatably supports the main shaft 20. The bearing 60 is a rolling bearing. In this embodiment, the bearing 60 is constituted by a ball bearing or a roller bearing having a contact angle (such as a tapered roller bearing or a self-aligning roller bearing). The bearing 60 may be a single row or double row.

  The displacement sensor 70 is a sensor for detecting the relative displacement between the inner and outer rings of the bearing 60. The displacement sensor 70 is fixed to the housing of the bearing 60 and outputs a detection value to the data processing device 80.

  FIG. 2 is a diagram for explaining an installation example of the displacement sensor 70. Referring to FIG. 2, the displacement sensor 70 is fixed to the side portion 65 of the housing 64 to which the outer ring 62 (stationary ring) of the bearing 60 is fixed, and is disposed in the vicinity of the main shaft 20. The relative displacement of the inner ring 61 (rotating wheel) in the rotation axis direction with respect to the outer ring 62 is detected by measuring the displacement of the main shaft 20 in the direction of the rotation axis O with respect to the portion 65). The displacement sensor 70 is adjusted so that the amount of displacement when the rotation of the main shaft 20 is stopped (that is, the axial load on the bearing 60 is zero) is zero, and the displacement of the main shaft 20 accompanying the rotation of the main shaft 20 is reduced. A displacement in the direction of the rotation axis O is detected. The displacement sensor 70 is configured by, for example, a non-contact type such as an eddy current type, a contact type, or a video camera.

  Referring again to FIG. 1, data processing device 80 is provided in nacelle 90 and receives a detection value from displacement sensor 70. As described above, the detected value from the displacement sensor 70 indicates the relative displacement in the rotation axis direction between the inner and outer rings of the bearing 60. Then, the data processing device 80 performs an abnormality diagnosis of the bearing 60 based on the relative displacement in the rotation axis direction between the inner and outer rings of the bearing 60 by a method described later according to a preset program. The data processing device 80 corresponds to an embodiment of the “diagnostic unit” in the present invention.

  In the wind power generator 10, when the blade 30 is rotated by wind power, an axial load (a load in the rotation axis direction) is generated on the bearing 60 that supports the main shaft 20. In the abnormality diagnosis device according to this embodiment, abnormality diagnosis of bearing 60 is executed based on the axial displacement between the inner and outer rings (detected value of displacement sensor 70) generated in bearing 60 in accordance with the axial load. Hereinafter, the abnormality diagnosis of the bearing 60 will be described in detail.

  FIG. 3 is a diagram showing the relationship between the axial load in the bearing 60 and the origin movement amount in the rotation axis direction. Referring to FIG. 3, the horizontal axis indicates the axial load in bearing 60. The axial load depends on the rotation of the blade 30, and specifically depends on the amount of power generated by the generator 50 that receives the rotational force from the blade 30 and the rotational speed of the main shaft 20. An axial load of 0 corresponds to the rotation of the blade 30 being stopped. Further, the fact that the axial load is F2 corresponds to the wind power generator 10 performing a rated operation, and the fact that the axial load is F1 corresponds to operating at 50% of the rated operation. To do.

  The vertical axis indicates the amount of origin movement in the direction of the rotation axis. The origin movement amount in the direction of the rotation axis is the center point of the outer ring that is a stationary wheel (intersection of the center axis of the outer ring and the axial center plane of the outer ring) as the origin. This shows the amount of movement in the axial direction from the origin of the axis and the center of the inner ring in the axial direction. That is, the amount of movement of the origin in the direction of the rotation axis is detected by the displacement sensor 70 (FIGS. 1 and 2).

  In the figure, circles indicate data when the bearing 60 is normal (for example, immediately after the installation of the wind power generator 10), and triangles indicate data when the bearing 60 is abnormal (the bearing 60 is damaged). Show. Whether the bearing 60 is normal or abnormal, the amount of movement of the origin in the direction of the rotation axis increases as the axial load increases. Further, when the bearing is damaged, the internal clearance becomes large, so that the axial origin movement amount for the same axial load is greater when the bearing 60 is abnormal than when it is normal. Therefore, the abnormality of the bearing 60 can be diagnosed by comparing the axial origin movement amount under a certain axial load condition (for example, F2 in the figure) with that at the normal time.

  Here, the amount of movement of the origin in the axial direction is detected by the displacement sensor 70, and the measured value of the displacement sensor 70 can change before and after the maintenance and repair of the equipment. That is, with the maintenance and repair of the equipment, the installation state of the displacement sensor 70 and the measurement target is adjusted, or the displacement sensor 70 is calibrated, so that the measured value (absolute Value) can vary. Such a change in the measured value of the displacement sensor 70 before and after maintenance may reduce the accuracy of abnormality diagnosis based on the displacement sensor 70.

  Therefore, in the abnormality diagnosis device according to this embodiment, abnormality diagnosis is performed based on the difference between the detection values of displacement sensor 70 under two different axial load conditions for bearing 60. That is, the difference between the detection value of the displacement sensor 70 under a predetermined first axial load condition and the detection value of the displacement sensor 70 under a predetermined second axial load condition different from the first axial load (hereinafter referred to as “displacement”). The data at the time of diagnosis is compared with the data at the time of normal. For example, when the displacement difference at the time of diagnosis is k times the displacement difference at the normal time (k> 1), it is diagnosed that the bearing 60 is abnormal.

  Specifically, for example, the axial load (= 0) when the rotation of the blade 30 is stopped is set as the first axial load condition, and the axial load (for example, when the wind turbine generator 10 is operating at a rated value). Displacement based on the detection value of the displacement sensor 70 under the first and second axial load conditions immediately after installation of the wind turbine generator 10 (before maintenance or repair) with F2) in the figure as the second axial load condition The difference Δδn is calculated and recorded as normal data. Then, the displacement difference Δδ is calculated again based on the detection value of the displacement sensor 70 under the first and second axial load conditions at the time of abnormality diagnosis (may be after maintenance), and the displacement difference Δδ is normal. The bearing 60 is diagnosed as abnormal when it is, for example, k times the displacement difference Δδn.

  As described above, by using the difference (displacement difference) in the origin movement amount in the rotation axis direction under the two different axial load conditions, unless maintenance or repair is performed between the measurements under the two axial load conditions. Even if maintenance and repairs are performed after normal data is collected, the accuracy of abnormality diagnosis is hardly affected.

  In the abnormality diagnosis apparatus according to this embodiment, an axial load is used as a load condition. Since the axial load depends on the rotation of the blade 30, according to the abnormality diagnosis apparatus, two different axial load conditions can be easily set as described above (when the rotation of the blade 30 is stopped / at the rated operation).

  FIG. 4 is a flowchart for explaining the procedure of normal data collection processing executed by the data processing device 80. The process shown in this flowchart is called from the main routine and executed every predetermined time or when a predetermined condition is satisfied.

  Referring to FIG. 4, the data processing device 80 determines whether or not normal data to be compared with data collected at the time of abnormality diagnosis has been collected (step S10). Whether or not the data has been collected is determined based on a normal data collection flag (set to off when the equipment is installed and turned on in step S90 described later). If it is determined that data has been collected (YES in step S10), data processing device 80 proceeds to step S100 without executing a series of subsequent processes.

  If it is determined in step S10 that data collection has not been completed (NO in step S10), the data processing device 80 determines whether rotation of the blade 30 is stopped (step S20). This determination process is a process for determining whether or not the first axial load condition is satisfied.

  If it is determined that the rotation of the blade 30 is stopped (YES in step S20), it is determined that the first axial load condition is satisfied, and the data processing device 80 uses the detection value of the displacement sensor 70 as a result. Record as δn0 in a recording device (not shown; the same applies hereinafter) (step S30). If it is determined in step S20 that the blade 30 is rotating (NO in step S20), the process proceeds to step S40 without executing the process in step S30.

  Next, the data processing device 80 determines whether or not the wind power generator 10 is in a rated operation (step S40). This determination process is a process for determining whether or not the second axial load condition is satisfied. Whether or not the rated operation is being performed can be determined based on, for example, the power generation amount of the generator 50 and the rotational speed of the blade 30 (the main shaft 20).

  If it is determined that wind power generator 10 is in rated operation (YES in step S40), it is determined that the second axial load condition is satisfied, and data processing device 80 detects the value detected by displacement sensor 70. Is recorded in the recording device as δn1 (step S50). If it is determined in step S40 that the rated operation is not being performed (NO in step S40), the process proceeds to step S60 without executing the process in step S50.

  Subsequently, the data processing device 80 determines whether or not the detection values δn0 and δn1 detected by the displacement sensor 70 are recorded in the recording device (step S60). When at least one of detection values δn0 and δn1 is not recorded (NO in step S60), the subsequent process is not executed and the process proceeds to step S100.

  If it is determined in step S60 that both detected values δn0 and δn1 are recorded (YES in step S60), data processing device 80 detects detected value δn1 under the second axial load condition and first axial value. A displacement difference Δδn indicating a difference from the detected value δn0 under the load condition is calculated (step S70). Then, the data processing device 80 records the displacement difference Δδn indicating the normal data on the recording device (step S80), and turns on the normal data collection flag (step S90).

  FIG. 5 is a flowchart for explaining the procedure of the abnormality diagnosis process executed by the data processing device 80. The process shown in this flowchart is also called and executed from the main routine at predetermined time intervals or when a predetermined condition is satisfied.

  Referring to FIG. 5, the data processing device 80 determines whether or not the normal time data collection flag is on (step S105). When the normal data collection flag is off (NO in step S105), data processing device 80 shifts the process to step S190 without executing a series of subsequent processes.

  If it is determined in step S105 that the normal data collection flag is on (YES in step S105), the data processing device 80 determines whether or not the rotation of the blade 30 is stopped (step S110). If it is determined that the rotation of the blade 30 is stopped (YES in step S110), it is determined that the first axial load condition is satisfied, and the data processing device 80 uses the detection value of the displacement sensor 70 as a result. Record as δ0 in the recording device (step S120). If it is determined in step S110 that the blade 30 is rotating (NO in step S110), the process proceeds to step S130 without executing the process in step S120.

  Next, the data processing device 80 determines whether or not the wind power generator 10 is in a rated operation (step S130). If it is determined that wind power generator 10 is in rated operation (YES in step S130), it is determined that the second axial load condition is satisfied, and data processing device 80 detects the value detected by displacement sensor 70. Is recorded in the recording device as δ1 (step S140). If it is determined in step S130 that the rated operation is not being performed (NO in step S130), the process proceeds to step S150 without executing the process in step S140.

  Subsequently, the data processing device 80 determines whether or not the detection values δ0 and δ1 detected by the displacement sensor 70 are recorded in the recording device (step S150). When at least one of detection values δ0 and δ1 is not recorded (NO in step S150), the subsequent process is not executed and the process proceeds to step S190.

  If it is determined in step S150 that both detected values δ0 and δ1 are recorded (YES in step S150), data processing device 80 detects detected value δ1 under the second axial load condition and first axial value. A displacement difference Δδ indicating a difference from the detected value δ0 under the load condition is calculated (step S160).

  Next, the data processing device 80 determines whether or not the displacement difference Δδ calculated in step S160 is larger than the threshold value δcr (step S170). This threshold value δcr is a threshold value for determining whether or not the bearing 60 is abnormal. For example, a displacement difference indicating normal data collected in the normal data collection process shown in FIG. It is determined based on Δδn. As an example, the threshold value δcr is set to k times the normal displacement difference Δδn (k> 1).

  If it is determined in step S170 that displacement difference Δδ is greater than threshold value δcr (YES in step S170), data processing device 80 outputs an alarm indicating that the abnormality diagnosis result is “abnormal” (step S170). Step S180). On the other hand, when it is determined in step S170 that displacement difference Δδ is equal to or smaller than threshold value δcr (NO in step S170), data processing device 80 proceeds to step S190 without executing step S180. To do.

  As described above, in this embodiment, abnormality diagnosis is executed based on the difference between the detection values of the displacement sensor 70 under two different first and second axial load conditions. By using the difference in displacement under different axial load conditions, even if equipment maintenance is performed after collecting normal data, which is comparative data for diagnosing abnormalities, two different load conditions If it is not performed during the measurement, the change in the detection value of the displacement sensor 70 accompanying the maintenance or the like hardly affects the abnormality diagnosis result. Further, in this abnormality diagnosis apparatus, two different load conditions can be easily set by using the axial load condition. For example, in the bearing 60 of the wind turbine generator 10, the rotation of the blade 30 and the axial load of the bearing 60 are correlated, and two different axial load conditions can be easily set based on the rotation state of the blade 30. Therefore, according to this embodiment, even if the installation state of the displacement sensor 70 and the measurement object changes due to maintenance or repair, it is possible to realize a highly accurate abnormality diagnosis.

[Modification 1]
In the above embodiment, the abnormality diagnosis is performed by comparing the axial displacement between the inner and outer rings of the bearing 60 with respect to the axial load by comparing with the data when the bearing 60 is normal (FIG. 3). An abnormality diagnosis may be performed by comparing the displacement in the bearing radial direction (direction perpendicular to the rotation axis) between the inner and outer rings with respect to the normal data.

  Referring to FIG. 2 again, in the first modification, the displacement sensor 70 measures the displacement of the main shaft 20 in the direction perpendicular to the rotation axis of the main shaft 20 with respect to the housing 64 (side portion 65), whereby the bearing radius of the inner ring 61 with respect to the outer ring 62 is measured. Detect relative displacement in direction. The displacement sensor 70 is disposed, for example, vertically below the main shaft 20 and detects a displacement in the vertical direction of the main shaft 20 accompanying the rotation of the main shaft 20.

  FIG. 6 is a diagram showing the relationship between the axial load in the bearing 60 and the origin movement amount in the bearing radial direction. Referring to FIG. 6, the horizontal axis indicates the axial load in bearing 60. The vertical axis indicates the amount of movement of the origin in the radial direction of the bearing 60. Here, the amount of movement of the origin in the bearing radial direction indicates the amount of movement of the center axis of the inner ring, which is a rotating wheel, in the vertically downward direction with the center axis of the outer ring, which is a stationary wheel, as the origin.

  In the drawing, circles indicate data when the bearing 60 is normal, and triangles indicate data when the bearing 60 is abnormal. Regardless of whether the bearing 60 is normal or abnormal, when the axial load is zero (when the spindle 20 stops rotating), the radial origin movement amount becomes maximum, and the radial origin movement amount increases as the axial load increases. Becomes smaller. Here, when the bearing is damaged, the internal clearance increases, so the origin movement in the radial direction when the axial load is small is larger than normal, and the change in the origin movement with the increase in axial load is normal. It is larger than the time (Δδa (when abnormal)> Δδn (when normal)).

  Therefore, the origin movement amount in the radial direction under a predetermined first axial load condition (for example, load 0) and the radius under a predetermined second axial load condition (for example, F2 in the drawing) different from the first axial load. The displacement difference Δδ at the time of diagnosis is compared with the displacement difference Δδn at the normal time with respect to the difference (displacement difference) from the origin movement amount in the direction. For example, the displacement difference Δδ at the time of diagnosis is k times ( It may be diagnosed that the bearing 60 is abnormal when k> 1).

  3 and 6, the difference in the origin movement amount under different axial load conditions is larger in the rotation axis direction than in the bearing radial direction. Therefore, the displacement sensor 70 measures the displacement in the rotation axis direction. It can be said that the abnormality diagnosis can be performed with higher accuracy than the method according to the first modification.

[Modification 2]
In the above embodiment, in the abnormality diagnosis process (FIG. 5) executed by the data processing device 80, the first axial load condition is satisfied when the rotation of the blade 30 is stopped, and the wind turbine generator 10 is Although the second axial load condition is satisfied during the rated operation, the first and second axial load conditions are not limited to this. The axial load depends on the rotation of the blade 30 (the larger the wind power, the faster the blade 30 rotates and the larger the axial load). It has a correlation with the rotational speed of the main shaft 20. Therefore, whether or not a predetermined axial load condition is satisfied may be determined based on the power generation amount P of the generator 50 and the rotational speed N of the main shaft 20 (blade 30) itself.

  FIG. 7 is a flowchart for explaining the procedure of the abnormality diagnosis process when it is determined whether or not the axial load condition is satisfied based on the power generation amount of the generator 50. The process shown in this flowchart is also called and executed from the main routine at predetermined time intervals or when a predetermined condition is satisfied.

  Referring to FIG. 7, this flowchart includes steps S112 and S132 instead of steps S110 and S130 in the flowchart shown in FIG. That is, when it is determined in step S105 that the normal time data collection flag is on (YES in step S105), data processing device 80 determines whether or not power generation amount P of generator 50 is smaller than threshold value P0. Is determined (step S112). This threshold value P0 is set to a relatively small value with respect to the rated power generation amount of the generator 50, and is set to, for example, about 10 to 20% of the rated power generation amount.

  If it is determined that the power generation amount P is smaller than the threshold value P0 (YES in step S112), it is determined that the first axial load condition is satisfied, and the data processing device 80 determines that the displacement sensor 70 Is recorded in the recording device as δ0 (step S120).

  Further, the data processing device 80 determines whether or not the power generation amount P of the generator 50 is larger than the threshold value P1 (step S132). This threshold value P1 is set to a value close to the rated power generation amount of the generator 50, for example, about 80 to 90% of the rated power generation amount.

  If it is determined that the power generation amount P is larger than the threshold value P1 (YES in step S132), it is determined that the second axial load condition is satisfied, and the data processing device 80 determines that the displacement sensor 70 Is recorded in the recording device as δ1 (step S140).

The other processing is the same as the processing described in the flowchart shown in FIG.
FIG. 8 is a flowchart for explaining the procedure of the abnormality diagnosis process when it is determined whether or not the axial load condition is satisfied based on the rotational speed of the main shaft 20. The process shown in this flowchart is also called and executed from the main routine at predetermined time intervals or when a predetermined condition is satisfied.

  Referring to FIG. 8, this flowchart includes steps S114 and S134 instead of steps S110 and S130 in the flowchart shown in FIG. That is, if it is determined in step S105 that the normal time data collection flag is on (YES in step S105), data processing device 80 determines whether or not rotational speed N of spindle 20 is lower than threshold value N0. Determination is made (step S112). The threshold value N0 is set to a relatively small value with respect to the rated rotational speed of the main shaft 20, and is set to about 10 to 20% of the rated rotational speed, for example.

  Then, if it is determined that rotational speed N is lower than threshold value N0 (YES in step S114), it is determined that the first axial load condition is satisfied, and data processing device 80 includes displacement sensor 70. Is recorded in the recording device as δ0 (step S120).

  Further, the data processing device 80 determines whether or not the rotational speed N of the spindle 20 is higher than the threshold value N0 (step S134). This threshold value N1 is set to a value close to the rated rotational speed of the main shaft 20, and is set to about 80 to 90% of the rated rotational speed, for example.

  Then, if it is determined that rotational speed N is higher than threshold value N0 (YES in step S134), it is determined that the second axial load condition is satisfied, and data processing device 80 includes displacement sensor 70. Is recorded in the recording device as δ1 (step S140).

The other processing is the same as the processing described in the flowchart shown in FIG.
In the second modification, the displacement sensor 70 may detect an axial displacement between the inner and outer rings of the bearing 60 to make an abnormality diagnosis, or the displacement sensor 70 may perform a bearing diagnosis between the inner and outer rings of the bearing 60. An abnormality diagnosis may be performed by detecting a radial displacement.

  Referring again to FIG. 1, in the above-described embodiment and modification, the displacement sensor 70 is attached to the bearing 60 that supports the main shaft 20, and performs an abnormality diagnosis of the bearing 60. 60 or in place of the bearing 60, a displacement sensor is installed in a bearing provided in the speed increaser 40 or the generator 50, and the inside of the speed increaser 40 is obtained in the same manner as in the above-described embodiment or modification. And abnormality diagnosis of the bearing provided in the generator 50 can be performed.

  In the above-described embodiment and modification, the case where the rolling bearing abnormality diagnosis device according to the present invention is applied to a wind power generator has been described. However, the application range of the present invention is not necessarily limited to a wind power generator. It is not a thing.

  The present invention does not exclude abnormality diagnosis using a vibration sensor such as an acceleration sensor. By using the abnormality diagnosis according to the present invention together with the abnormality diagnosis using the vibration sensor, it is possible to pursue a more accurate abnormality diagnosis.

  The embodiments disclosed this time are also scheduled to be implemented in appropriate combinations. The embodiment disclosed this time should be considered as illustrative in all points and not restrictive. The scope of the present invention is shown not by the above description of the embodiment but by the scope of claims, and is intended to include all modifications within the meaning and scope equivalent to the scope of claims.

  DESCRIPTION OF SYMBOLS 10 Wind power generator, 20 Main shaft, 30 Blade, 40 Speed increaser, 50 Generator, 60 Bearing, 61 Inner ring, 62 Outer ring, 63 Roller, 64 Housing, 65 Side part, 70 Displacement sensor, 80 Data processing apparatus, 90 Nacelle , 100 tower.

Claims (7)

An abnormality diagnosis device for a rolling bearing, wherein the rolling bearing is constituted by a ball bearing or a roller bearing having a contact angle,
A displacement sensor for detecting a relative displacement between the inner and outer rings of the rolling bearing;
A diagnosis unit that performs an abnormality diagnosis of the rolling bearing based on a detection value of the displacement sensor;
The diagnosis unit detects a value detected by the displacement sensor under a first axial load condition for the rolling bearing and a detection by the displacement sensor under a second axial load condition different from the first axial load condition. An abnormality diagnosis device for a rolling bearing that executes the abnormality diagnosis based on a difference from the value. The rolling bearing is used as a main bearing of a wind power generator,
The abnormality diagnosis device for a rolling bearing according to claim 1, wherein the first and second axial load conditions are determined based on a power generation amount of the wind turbine generator.   2. The abnormality diagnosis device for a rolling bearing according to claim 1, wherein the first and second axial load conditions are determined based on a rotational speed of the rolling bearing. The rolling bearing is used as a main bearing of a wind power generator,
The first axial load condition is satisfied when the rotation of the blade of the wind turbine generator is stopped,
The abnormality diagnosis device for a rolling bearing according to claim 1, wherein the second axial load condition is satisfied when the wind turbine generator is performing a rated operation.   5. The abnormality diagnosis device for a rolling bearing according to claim 1, wherein the displacement sensor detects a relative displacement between the inner and outer rings in a rotation axis direction of the rolling bearing. A blade that receives wind,
A main shaft connected to the blade;
A generator connected to the main shaft or a speed increaser for increasing the rotation of the main shaft;
A plurality of rolling bearings provided on the main shaft, the speed increaser and the generator;
An abnormality diagnosing device for diagnosing at least one abnormality of the plurality of rolling bearings,
The target bearing to be subjected to abnormality diagnosis by the abnormality diagnosis device is constituted by a ball bearing or a roller bearing having a contact angle,
The abnormality diagnosis device includes:
A displacement sensor for detecting a relative displacement between the inner and outer rings of the target bearing;
A diagnostic unit that performs an abnormality diagnosis of the target bearing based on a detection value of the displacement sensor,
The diagnosis unit detects a value detected by the displacement sensor under a first axial load condition for the target bearing and a detection by the displacement sensor under a second axial load condition different from the first axial load condition. A wind turbine generator that performs the abnormality diagnosis based on a difference from the value. An abnormality diagnosis method for a rolling bearing, wherein the rolling bearing is constituted by a ball bearing or a roller bearing having a contact angle,
Detecting a first displacement amount indicating a relative displacement between the inner and outer rings of the rolling bearing under a first axial load condition with respect to the rolling bearing;
Detecting a second displacement amount indicating the relative displacement under a second axial load condition different from the first axial load condition;
An abnormality diagnosis method for a rolling bearing, comprising: diagnosing an abnormality of the rolling bearing based on a difference between the first displacement amount and the second displacement amount.

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